2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第2页

LIE operators for compressive sensing 压缩感知的LIE算子

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-07-14 DOI: 10.1109/ICASSP.2014.6854018

C. Hegde, Aswin C. Sankaranarayanan, Richard Baraniuk

引用次数: 1

A statistical evaluation of Sparsity-based Distance Measure (SDM) as an image quality assessment algorithm 基于稀疏性的距离度量(SDM)作为图像质量评估算法的统计评价

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-07-14 DOI: 10.1109/ICASSP.2014.6854108

K. Priya, K. Manasa, Sumohana S. Channappayya

引用次数: 4

A discriminatively trained Hough Transform for frame-level phoneme recognition 基于判别训练的Hough变换的帧级音素识别

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-07-14 DOI: 10.1109/ICASSP.2014.6854053

J. Dennis, T. H. Dat, Haizhou Li, Chng Eng Siong

{"title":"A discriminatively trained Hough Transform for frame-level phoneme recognition","authors":"J. Dennis, T. H. Dat, Haizhou Li, Chng Eng Siong","doi":"10.1109/ICASSP.2014.6854053","DOIUrl":"https://doi.org/10.1109/ICASSP.2014.6854053","url":null,"abstract":"Despite recent advances in the use of Artificial Neural Network (ANN) architectures for automatic speech recognition (ASR), relatively little attention has been given to using feature inputs beyond MFCCs in such systems. In this paper, we propose an alternative to conventional MFCC or filterbank features, using an approach based on the Generalised Hough Transform (GHT). The GHT is a common approach used in the field of image processing for the task of object detection, where the idea is to learn the spatial distribution of a codebook of feature information relative to the location of the target class. During recognition, a simple weighted summation of the codebook activations is commonly used to detect the presence of the target classes. Here we propose to learn the weighting discriminatively in an ANN, where the aim is to optimise the static phone classification error at the output of the network. As such an ANN is common to hybrid ASR architectures, the output activations from the GHT can be considered as a novel feature for ASR. Experimental results on the TIMIT phoneme recognition task demonstrate the state-of-the-art performance of the approach.","PeriodicalId":6545,"journal":{"name":"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"6 12 1","pages":"2514-2518"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83841263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

On the convergence of average consensus with generalized metropolis-hasting weights 广义大都市加速权下平均一致性的收敛性

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-07-14 DOI: 10.1109/ICASSP.2014.6854643

V. Schwarz, Gabor Hannak, G. Matz

引用次数: 24

Unsupervised domain adaptation for deep neural network based voice activity detection 基于深度神经网络的无监督域自适应语音活动检测

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-07-14 DOI: 10.1109/ICASSP.2014.6854930

Xiao-Lei Zhang

引用次数: 9

Improvement of utterance clustering by using employees' sound and area data 基于员工声音和区域数据的语音聚类改进

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-07-14 DOI: 10.1109/ICASSP.2014.6854160

Tetsuya Kawase, Masanori Takehara, S. Tamura, S. Hayamizu, Ryuhei Tenmoku, T. Kurata

引用次数: 2

Image denoising by targeted external databases 针对外部数据库进行图像去噪

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-07-14 DOI: 10.1109/ICASSP.2014.6854040

Enming Luo, Stanley H. Chan, Truong Q. Nguyen

引用次数: 24

Automatic initialization for naval application of graph segmentation techniques: A comparative study 图形分割技术在舰船中的自动初始化应用:比较研究

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-07-14 DOI: 10.1109/ICASSP.2014.6854578

Irene Camino, U. Zölzer

引用次数: 0

A maximal figure-of-merit learning approach to maximizing mean average precision with deep neural network based classifiers 基于深度神经网络的分类器平均精度最大化的最大优值学习方法

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-07-14 DOI: 10.1109/ICASSP.2014.6854454

Kehuang Li, Zhen Huang, You-Chi Cheng, Chin-Hui Lee

{"title":"A maximal figure-of-merit learning approach to maximizing mean average precision with deep neural network based classifiers","authors":"Kehuang Li, Zhen Huang, You-Chi Cheng, Chin-Hui Lee","doi":"10.1109/ICASSP.2014.6854454","DOIUrl":"https://doi.org/10.1109/ICASSP.2014.6854454","url":null,"abstract":"We propose a maximal figure-of-merit (MFoM) learning framework to directly maximize mean average precision (MAP) which is a key performance metric in many multi-class classification tasks. Conventional classifiers based on support vector machines cannot be easily adopted to optimize the MAP metric. On the other hand, classifiers based on deep neural networks (DNNs) have recently been shown to deliver a great discrimination capability in automatic speech recognition and image classification as well. However, DNNs are usually optimized with the minimum cross entropy criterion. In contrast to most conventional classification methods, our proposed approach can be formulated to embed DNNs and MAP into the objective function to be optimized during training. The combination of the proposed maximum MAP (MMAP) technique and DNNs introduces nonlinearity to the linear discriminant function (LDF) in order to increase the flexibility and discriminant power of the original MFoM-trained LDF based classifiers. Tested on both automatic image annotation and audio event classification, the experimental results show consistent improvements of MAP on both datasets when compared with other state-of-the-art classifiers without using MMAP.","PeriodicalId":6545,"journal":{"name":"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"4503-4507"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79662591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 40

MIMO detection based on averaging Gaussian projections 基于高斯投影平均的MIMO检测

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-07-14 DOI: 10.1109/ICASSP.2014.6853932

J. Goldberger

引用次数: 2